Benchmarking and Comparing AD-Related AI Methods Across Sites on a Standardized Dataset

在标准化数据集上跨站点对 AD 相关 AI 方法进行基准测试和比较

• 批准号: 10825403
• 负责人: Christos Davatzikos
• 金额: $ 35.75万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10825403
• 关键词: Acceleration; Address; Administrative Supplement; Algorithms; Alzheimer' s Disease; Alzheimer' s disease risk; Award; Benchmarking; Big Data Methods; Biological; Biological Markers; Clinical; Clinical Data; Clinical Trials; Cognitive; Collaborations; Communities; Coupled; Data; Data Collection; Data Set; Data Sources; Databases; Dedications; Dimensions; Disease; Funding; Genetic; Genetic Diseases; Genetic Markers; Genomics; Goals; Grant; Image; Informatics; International; Leadership; Literature; Machine Learning; Magnetic Resonance Imaging; Methods; Modeling; Molecular; Outcome Measure; Parents; Pharmaceutical Preparations; Process; Prognosis; Research; Site; Software Tools; Speed; Standardization; Structure; Suggestion; Systems Analysis; Testing; Training; U-Series Cooperative Agreements; United States National Institutes of Health; Vision; Work; analytical method; analytical tool; artificial intelligence algorithm; biobank; brain magnetic resonance imaging; clinical predictors; cognitive system; data harmonization; data integration; data standards; deep learning; design; drug development; drug repurposing; endophenotype; experience; genetic association; genome wide association study; genomic data; imaging genetics; large scale data; machine learning method; neurobiological mechanism; neuroimaging; novel; outcome prediction; phenotypic data; prediction algorithm; repository; response; synergism; tool; translational impact; whole genome

Project Summary In response to PAR-19-269 “Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data (U01 Clinical Trial Not Allowed)”, our project unites experts in AD genomics, machine learning and AI (including deep learning), large-scale data integration, and international data harmonization to work in a carefully-designed Consortium Structure in close partnership with the NIH, ADSP, and NIAGADS. We will develop a suite of complementary big data analytic approaches for ultra-scale analysis of Alzheimer’s Disease (AD) genomic and phenotypic data. The vast data volumes now generated by the Alzheimer’s Disease Sequencing Project (ADSP), National Alzheimer’s Coordinating Center (NACC), Alzheimer’s Disease Neuroimaging Initiative (ADNI), Accelerating Medications Partnership AD (AMP-AD), and UK Biobank (UKBB), far exceed the capacity of all current analytic methods, which have not kept pace with the scale and speed of data collection. This vast amount of genetic and phenotypic data mandates new and more powerful algorithms to: (1) store, manage, and manipulate whole-genome sequences and associated data on an ever-growing scale; (2) discover novel AD risk and protective loci by merging informatics and AD genomics databases; (3) relate whole-genome changes to the ATN(v) biomarkers that now define biological AD. Our Ultrascale Machine Learning Initiative, or “ULTRA” - will offer new AI and deep learning tools to discover features in massive scale genomics data - relating whole genome data to biomarker features by merging all relevant data sources. Our team of experienced PIs will coordinate efforts across the U.S. to create these large-scale data analytic tools. Our MPI team and 6 Core Leads have decades of experience working together and with the AD community in pioneering machine learning methods for AD genetics and neuroimaging, including leadership of international neuroimaging consortia across the world. Dedicated Cores focus on Genomic, Imaging, and Cognitive Data Harmonization. Curated data will then be efficiently imported into AI approaches and informatics pipelines that will allow the AD research community to leverage ultra-scale, multidimensional genomic and phenotypic data from the ADSP, NACC, ADNI, AMP-AD, and others. Our work is organized by a carefully-designed and coordinated Consortium guided by all stake-holders, clinical leaders, and pioneering analysts in AD genomics and neuroimaging. Our ultrascale AI tools will advance AD genomics research and will include efforts in training, and a dedicated Drug Repurposing Core. This team effort will accelerate understanding of the genetic, molecular and neurobiological mechanisms of AD, yielding significant translational impact on disease and drug development.

项目总结

项目成果

Sex Differences in Cognitive Decline in Subjects with High Likelihood of Mild Cognitive Impairment due to Alzheimer's disease.

• DOI: 10.1038/s41598-018-25377-w
• 发表时间: 2018-05-10
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Sohn D;Shpanskaya K;Lucas JE;Petrella JR;Saykin AJ;Tanzi RE;Samatova NF;Doraiswamy PM
• 通讯作者: Doraiswamy PM

Attention-based multiple instance learning with self-supervision to predict microsatellite instability in colorectal cancer from histology whole-slide images.

• DOI: 10.1109/embc48229.2022.9871553
• 发表时间: 2022-07-01
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Leiby, Jacob S;Hao, Jie;Kim, Dokyoon
• 通讯作者: Kim, Dokyoon